第二阶段 - HazPrep 工人培训 - 社区风险概况 - COVID-19 快速响应-猫眼课题宝

第二阶段 - HazPrep 工人培训 - 社区风险概况 - COVID-19 快速响应

批准号：

10157843

负责人：

Henry Ryng

金额：

$8.6万

依托单位：

INXSOL, LLC

依托单位国家：

美国

项目类别：

财政年份：

2017

资助国家：

美国

项目状态：

已结题

起止时间：

2017-06-01 至 2021-05-31

来源：

https://reporter.nih.gov/project-details/10157843

关键词：

Academy Achievement Algorithms Assessment tool Awareness Big Data COVID-19 COVID-19 pandemic Case Study Communities Community of Practice Consultations Containment Credentialing Data Effectiveness Emergency response Employee Event Excision Exercise Family Feasibility Studies Feedback Feeds Formulation Frequencies Generations Hazardous Substances Health Health Sciences Individual Industry Infection prevention Ingestion Knowledge Libraries Location Machine Learning Maintenance Metadata Modeling Occupations Performance Phase Plants Prevention Privatization Process Production Readiness Recording of previous events Resources Risk S Phase Safety Site Source Structure Surveys System Taxonomy Techniques Technology Testing Texas Time Training Transportation Universities Work application programming interface commercialization crowdsourcing dashboard data archive design e-commerce experience feeding flexibility hazard innovation learning strategy machine learning algorithm pandemic disease personalized learning prototype repaired response skills social learning sound tool virtual wasting web site

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PROJECT SUMMARY/ABSTRACT COVID-19 Supplement to HazPrep Phase II. Original Summary: A worker's personal hazard profile (PHP) is a function of hazards present and his/her exposure level to those hazards. Workers with an elevated level of risk are those who can be engaged in activities related to - or working around - hazardous materials, waste generation, removal, containment, transportation, and emergency response. inXsol’s Phase I feasibility study verified the appeal and effectiveness of a new form of crowdsourced social learning platform. Our approach using cloud technology creates a dynamically growing library of incidents/scenarios, highly personalized (occupation/task/geo) risk profile and generates learning activities to train on risk awareness and mitigation techniques. The Phase II proposal includes implementation of an innovative use of big data algorithms for community profiles and fusion with PHP allowing for targeted and personalized training completing the HazPrep prototype developed and exercised by our beta test team in Phase I.

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